ESTRO 36 Abstract Book

S169 ESTRO 36 _______________________________________________________________________________________________

This retrospective cohort study includes consecutive OC patients staged with PET/CT between October 2010 and December 2014. PET-defined tumour variables and texture metrics were obtained using ATLAAS, a machine learning algorithm for optimised automatic segmentation. A Cox regression model including age, radiological stage, treatment and 12 novel texture variables was developed and a prognostic score stratifying patients into quartiles was calculated. Primary outcome was OS and a p-value <0.1 was considered statistically significant. Results Three hundred and forty-three consecutive patients [median age=67 (range=24-83), adenocarcinoma=258] were included. Median survival was 17 months (95% CI 14.685–19.315). Multivariate analysis demonstrated 7 variables that were significantly and independently associated with OS; age [HR=1.024 (95% CI 1.010-1.038), p<0.001], radiological stage [HR=1.492 (1.221-1.823), p<0.001], treatment [HR=2.855 (2.038–3.998), p<0.001], standard deviation [HR=0.898 (0.815–0.989), p=0.029], log(coarseness) [HR=1.774 (0.918–3.43), p=0.088], dissimilarity [HR=1.136 (1.007–1.281), p=0.038] and zone percentage [HR=0.938 (0.897–0.980), p=0.005]. A prognostic score derived from the model equation showed significant differences in OS between quartiles (X 2 =90.13, df=3, p<0.001).

Mean(SD) of change in GTV expressed as percentage

Time Points

Baseline and different fractions Fractions 0 and 5 Fractions 0 and 10

-11.5%(7.8) -52.0%(4.6) -57.0%(6.0) -64.0%(9.0)

Fractions 0 and 15 Fractions 0 and 20

Fractions 0 and last fraction

-66.0%(.0)

Baseline

different

fractions Fractions 0 and 5 Fractions 5 and 10

-11.5%(7.8)

-48.0%(3.9) Fractions 10 and 15 -14.5%(5.5) Fractions 15 and 20 -16.0%(12.2) Fractions 20 and the last fraction -6.0%(5.9) Conclusion

Real-time MRI-guided radiation provides previously unavailable data on tumor response during neoadjuvant chemoradiation. In this study, the most significant volumetric change in the GTV was observed earlier than expected, between fractions 5 and 10. Correlation of early volumetric response changes with clinical and or pathological outcomes may prove highly valuable. Daily MRI during radiation provides a unique opportunity to tailor individual treatment based on early response to chemoradiation, and suggests that functional imaging correlates are likely best undertaken early during chemoradiation. Additional patients are being recruited into this study to correlate imaging response with clinical and pathological outcomes. PV-0323 Development of a prognostic model incorporating PET texture analysis in oesophageal cancer patients K. Foley 1 , R. Hills 1 , B. Berthon 2 , C. Marshall 2 , W. Lewis 3 , T. Crosby 4 , E. Spezi 5 , A. Roberts 6 1 Cardiff University, Division of Cancer & Genetics, Cardiff, United Kingdom 2 Cardiff University, Wales Research & Diagnostic PET Imaging Centre, Cardiff, United Kingdom 3 University Hospital of Wales, Upper GI Surgery, Cardiff, United Kingdom 4 Velindre Cancer Centre, Oncology, Cardiff, United Kingdom 5 Cardiff University, School of Engineering, Cardiff, United Kingdom 6 University Hospital of Wales, Clinical Radiology, Cardiff, United Kingdom Purpose or Objective Texture analysis provides additional quantitative data extracted from radiological staging investigations. This exploratory study investigates the prognostic significance of PET texture variables when incorporated into a model predicting overall survival (OS) in patients with oesophageal cancer (OC). Material and Methods

Conclusion This study demonstrates the additional benefit of PET texture analysis in OC staging, over and above the current TNM system. Our prognostic model requires further validation, but highlights the potential of texture analysis to predict survival in OC. PV-0324 FDG-PET based pelvic bone marrow dose predicts for blood cell nadirs in CT-RT for anal cancer P. Franco 1 , F. Arcadipane 1 , R. Ragona 1 , A. Lesca 2 , E. Gallio 3 , M. Mistrangelo 4 , P. Cassoni 5 , M. Baccega 2 , P. Racca 6 , R. Faletti 7 , N. Rondi 8 , M. Morino 4 , U. Ricardi 1 1 University of Turin A.O.U. Citta' della Salute e della Scienza, Department of Oncology- Radiation Oncology, Torino, Italy 2 A.O.U. Citta' della Salute e della Scienza- Turin, Department of Surgical Sciences - Nuclear Medicine, Torino, Italy 3 A.O.U. Citta' della Salute e della Scienza- Turin, Department of Medical Imaging - Medical Physics, Torino, Italy 4 University of Turin A.O.U. Citta' della Salute e della Scienza, Department of Surgical Sciences, Torino, Italy 5 University of Turin A.O.U. Citta' della Salute e della